Background　The　liver　is　an　important　organ　that　undertakes　the　metabolic　function　of　the　human　body.　Liver　cancer　has　become　one　of　the　cancers　with　the　highest　mortality.　In　clinic,　it　is　an　important　work　to　extract　the　liver　region　accurately　before　the　diagnosis　and　treatment　of　liver　lesions.　However,　manual　liver　segmentation　is　a　time-consuming　and　boring　process.　Not　only　that,　but　the　segmentation　results　usually　varies　from　person　to　person　due　to　different　work　experience.　In　order　to　assist　in　clinical　automatic　liver　segmentation,　this　paper　proposes　a　U-shaped　network　with　multi-scale　attention　mechanism　for　liver　organ　segmentation　in　CT　images,　which　is　called　MSA-UNet.　Our　method　makes　a　new　design　of　U-Net　encoder,　decoder,　skip　connection,　and　context　transition　structure.　These　structures　greatly　enhance　the　feature　extraction　ability　of　encoder　and　the　efficiency　of　decoder　to　recover　spatial　location　information.　We　have　designed　many　experiments　on　publicly　available　datasets　to　show　the　effectiveness　of　MSA-UNet.　Compared　with　some　other　advanced　segmentation　methods,　MSA-UNet　finally　achieved　the　best　segmentation　effect,　reaching　98.00%　dice　similarity　coefficient　(DSC)　and　96.08%　intersection　over　union　(IOU).